1. Technical Field
The present invention relates generally to a motion capture apparatus and method and, more particularly, to a motion capture apparatus and method, which generate a three-dimensional (3D) skeleton using a reconstructed 3D appearance and information about a previous frame, and search for global feature points, so that the precision of motion reconstruction is improved and the accumulation of errors is reduced, thus enabling complicated motion to be reconstructed and a long sequence of motions to be reconstructed.
2. Description of the Related Art
Prior technology for acquiring motion information about entities which engage in dynamic motion can be mainly classified into two types of methods.
First, there is a method using markers. As conventional motion reconstruction techniques, there have been mainly used methods of attaching markers to the appearance of an entity, the motion information of which is desired to be acquired, tracking the locations of the markers per frame, and then obtaining motion information. The motion information obtained using the markers undergoes a manual procedure that supplements erroneous portions caused by occlusion, sensor errors, etc., and then a final result is obtained. Such a marker-based method is advantageous in that relatively high precision can be obtained, but is disadvantageous in that a large number of markers must be attached to the appearance of an entity, expensive motion capture equipment must be provided, and the post-processing of the captured markers is required.
As the other method of reconstructing the motion of a dynamic entity, there is a marker-free method that does not use markers. This is also referred to as a markerless method. Compared to the above method using markers, the method that does not use markers is advantageous in that since there is no need to attach markers, capturing is conveniently performed, and in that since only an image sensor is used instead of expensive equipment in most cases, an advantage of low cost can be obtained from the standpoint of price. However, there is a disadvantage in that in the case of a complicated motion, it is difficult to extract exact motion.
Marker-free motion reconstruction algorithms can be mainly classified into two types depending on whether preliminary human model information has been used.
The first type is a model-free approach method that does not use a model. This method is configured such that a three-dimensional (3D) pose is extracted based on an image in most cases. This model-free approach method is divided into a bottom-up approach method that primarily finds the arms and legs of a body and extracts motion using probabilistic assemblies, and an example-based approach method that detects a pose by directly matching an image with a 3D pose on the basis of a pre-stored database (DB). However, the model-free approach method is disadvantageous in that in a complicated case, precision is poor, and, in particular, the example-based approach method is disadvantageous in that extractable motion is limited depending on DBs.
The second type is an approach method that uses a predefined model. In this case, appearance information about a predefined model, as well as kinematic information about the predefined model, can be utilized. Further, motion can be extracted by comparing a preliminary model with entities in an image or with a 3D volume entity obtained from a multi-view image via voxel reconstruction or the like. Compared to the existing model free method, this approach makes it possible to extract even a relatively complicated motion. However, even in this approach, how to derive corresponding relationships between a model and a 3D entity still remains the principal issue. Since most of the conventional approach methods depend on local optimization, the phenomenon of error accumulation is very prominent. Therefore, the existing approach methods are problematic in that in the case of a long sequence image containing complicated motion, it is impossible to complete the tracking of the image, and the motion may fail to be reconstructed because of the accumulation of errors during the tracking.